Philosophical Foundations for Causal Networks

Bayes nets are seeing increasing use in expert systems [2, 6], and structural equations models continue to be popular in many branches of the social sciences [1]. Both types of models involve directed acyclic graphs with variables as nodes, and in both cases there is much mysterious talk about causal interpretation. This paper uses probability trees to give precise conditions under which Bayes nets can be said to have a causal interpretation. Proofs and elaborations are provided by the author in [4].

• Type: Conference
• Year: 1994
• Where: IPMU
• Authors: Glenn Shafer
• Keywords: Bayes Nets, Causal Interpretation, Information Management, IPMU 1994, Structural Equations Models